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26 November 2025

Application of IoT in Monitoring Greenhouse Gas Emissions in Anaerobic Reactors

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1
Department of Electrical and Computer Engineering, University of California at Santa Cruz, Santa Cruz, CA 95064, USA
2
Department of Electrical and Computer Engineering, University of California at Davis, Davis, CA 95616, USA
3
Department of Population Health and Reproduction, School of Veterinary Medicine, University of California at Davis, Davis, CA 95616, USA
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This article belongs to the Special Issue Editorial Board Members’ Collection Series: Economic Growth, Energy Consumption and Carbon Emission

Abstract

Anaerobic reactors are often used to control emissions and capture greenhouse gas (GHG) (biogas, a mixture of carbon dioxide and methane) from waste such as dairy manure. However, real-time monitoring of biogas production during in vitro anaerobic experiments is often challenging mainly due to the unpredictable and low levels of biogas production in a lab reactor system. The application of Internet of Things (IoT) technologies can enhance real-time monitoring of biogas production and GHG emissions from livestock waste. Integration of IoT to anaerobic reactors provides transformative solutions for low-cost monitoring. In this study, an IoT based sensor system that included a highly sensitive Renesas mass flow sensor module for biogas monitoring, Adafruit ported pressure sensor for monitoring of reactor pressure, and ultra-small DROK temperature probe for temperature monitoring was built and implemented for determining the biogas production in anaerobic reactors. Further, impacts of anaerobic process on the reduction of pathogenic organisms such as E. coli were determined using the conventional culture-based method. Results showed that the application of the IoT based system was able to monitor biogas production in real-time, and transmit the data to mobile phone using the ThingSpeak IoT platform offered by MathWorks (MATLAB) (Natick, MA, USA). The difference between the sensor’s biogas volume readings and actual observations over a 30-day time interval was 5–6% indicating the high level of accuracy and low error levels of the system. Further, results showed 1.6–4.8 log reductions of E. coli in effluent of anaerobic reactors indicating substantial impacts of the anaerobic process on pathogen indicator reduction. We anticipate that the system we used in this study has a substantial potential to enhance monitoring of anaerobic reactors and GHG emissions from livestock waste.

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